Name
Abstract | ||
|---|---|---|
Gait velocity is an important measure of independence and functional ability to those within the older population. Detecting changes in gait velocity can aid to provide interventions to avoid hospitalisation, currently gait velocity is assessed in a clinical setting, where the patient is timed over a measured distance between 3-6 metres by a clinician, however, this is time consuming, subjective, and not possible to carry out frequently over time. An unobtrusive method of monitoring gait velocity, frequently, over extended periods of time, would therefore be advantageous when developing interventions. This paper proposes an unobtrusive computer vision-based method of continuously monitoring an occupants gait velocity within their own home. This is achieved through the use of a low cost thermal vision sensor. The system was benchmarked against the clinical standard method of being timed by a stopwatch. Results show a high correlation between the gait velocity measured by the thermal vision sensor and the measured stopwatch velocity (R=0.941, p=0.02). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/PERCOMW.2018.8480174 | 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) |
Keywords | Field | DocType |
Thermal vision sensors,Ambient assisted living,Smart homes,Gait recognition | Computer vision,Population,Gait,Computer science,Stopwatch,Artificial intelligence,Vision sensor,Distortion | Conference |
ISSN | ISBN | Citations |
2474-2503 | 978-1-5386-3228-4 | 0 |
PageRank | References | Authors |
0.34 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Javier Medina Quero | 1 | 2 | 1.38 |
| Colin Shewell | 2 | 6 | 1.92 |
| Ian Cleland | 3 | 98 | 23.12 |
| Joseph Rafferty | 4 | 54 | 10.70 |
| Chris D. Nugent | 5 | 1150 | 128.39 |
| Macarena Espinilla Estévez | 6 | 4 | 1.76 |